Software Tools for Earth and Environmental Science
– 10th WEEK –
Emir Toker
14/11/2019
Syllabus
Book
Book
DataCamp Class
NETCDF
NETCDF - Data
CRU_TR_Near-Surface_Temp_16-01-1901_16-12-2012_Monthly
NETCDF - Data
CRU_TR_Near-Surface_Temp_16-01-1901_16-12-2012_Monthly
R Advance - ncdf4 Package
R Advance - RNetCDF Package
R Advance - ncdf4 Package
- rnetcdf_time_julian <- as.Date(rnetcdf_time, origin=as.Date("1900-01-01"))
- ncdf4_time_julian <- as.Date(ncdf4_time, origin=as.Date("1900-01-01"))
- ncdf4_tmp <- ncvar_get(ncdf4_open,"tmp")
- ncdf4_tmp[,,1]
- ncdf4_tmp_mean <- ncdf4_tmp[,,mean(1:1344)]
- image(ncdf4_lon, ncdf4_lat, ncdf4_tmp_mean)
R Advance - ncdf4 Package
- install.packages("maptools")
- library("maptools")
- turkey_shp <- readShapePoly("turkiye.shp")
- image(rnetcdf_lon, rnetcdf_lat, ncdf4_tmp_mean)
- plot(turkey_shp, add = T)
R Advance - ncdf4 Package
- library("fields")
- image.plot( rnetcdf_lon, rnetcdf_lat, ncdf4_tmp_mean,
xlab = "Lon",
ylab = "Lat",
main = "Mean Temperature for Turkey" )
- plot(turkiye.shp, add = T) 
R Elemantary Statistics
R Elemantary Statistics
Centrality: Mean, Median, Mode
- Measures of centrality are commonly used to explain large collections of data by describing where numeric observations are centered.
R Elemantary Statistics
Centrality: Mean, Median, Mode
- Median : “middle magnitude” of your observations
0 . 0 . 0 . 0 . 0
o . o . o . o . o . o
R Elemantary Statistics
Centrality: Mean, Median, Mode
- Mode : Simply the “most common” observation.
Sample : 2 , 4.4 , 3 , 3 , 2 , 2.2 , 2 , 4
2 , 2 , 2 , 2.2 , 3 , 3 , 4, 4.4 ( n=8 , n/2 = 4) 
R Elemantary Statistics
Quantiles, Percentiles, and the Five-Number Summary
A quartile is a type of quantile.
R Elemantary Statistics
plot(xdata,type="n",xlab="",ylab="data vector",yaxt="n",bty="n")
abline(h=c(3,3.5),lty=2,col="red")
abline(v=2.825,lwd=2,lty=3)
text(c(0.8,0.8),c(3,3.5),labels=c("x","y"))
points(jitter(c(xdata,ydata)),c(rep(3,length(xdata)), rep(3.5,length(ydata))))
the observations in ydata are more “spread out”
R Elemantary Statistics
Spread: Variance, Standard Deviation, and the Interquartile Range
- The sample variance measures the degree of the spread of numeric observations around their arithmetic mean.
R Elemantary Statistics
Spread: Variance, Standard Deviation, and the Interquartile Range
2 , 4.4 , 3 , 3 , 2 , 2.2 , 2 , 4 ( mean = 2.825)
R Elemantary Statistics
Spread: Variance, Standard Deviation, and the Interquartile Range
- The standard deviation is simply the square root of the variance. The scale of the original observations.
0.953 represents the average distance of each observation from the mean
R Elemantary Statistics
Spread: Variance, Standard Deviation, and the Interquartile Range,
- Unlike the variance and standard deviation, the interquartile range (IQR) is not computed with respect to the sample mean.
- IQR is computed as the difference between the upper and lower quartiles of your data
R Elemantary Statistics
R Elemantary Statistics
R Elemantary Statistics
Covariance and Correlation
- Investigate the relationship between two numeric variables to assess trends
- The covariance expresses how much two numeric variables “change together” and the nature of that relationship, whether it is positive or negative.
R Elemantary Statistics
Covariance and Correlation
x = {x1,x2,…,xn}
y = {y1,y2,…,yn}
for i = 1,. . . ,n
When you get a positive result for rxy, it shows that there is a positive lin- ear relationship. When rxy = 0, this indicates that there is no linear relationship.
R Elemantary Statistics
Covariance and Correlation
x = {2,4.4,3,3,2,2.2,2,4}
y = {1,4.4,1,3,2,2.2,2,7}
mean x and y = 2.825
positive relationship
R Elemantary Statistics
Covariance and Correlation
- Correlation allows you to interpret the covariance further by identifying the strength of any association.
R Elemantary Statistics
Covariance and Correlation
Most common of these is Pearson’s product-moment correlation coefficient. (R default)
The correlation coefficient estimates the nature of the linear relationship between two sets of observations
−1 ≤ ρxy ≤ 1
ρxy = 1, which is a perfect positive linear relationship
R Elemantary Statistics
Covariance and Correlation
x = {2,4.4,3,3,2,2.2,2,4}
y = {1,4.4,1,3,2,2.2,2,7}
(mean x and y = 2.825)
(sx = 0.953 and sy = 2.013)
(rxy = 1.479)
ρxy is positive
R Elemantary Statistics
Covariance and Correlation
xdata <- c(2,4.4,3,3,2,2.2,2,4)
ydata <- c(1,4.4,1,3,2,2.2,2,7)
plot(xdata,ydata,pch=13,cex=1.5)
R Elemantary Statistics
R Elemantary Statistics
Basic Data Visualization
Barplots and Pie Charts
head(station_data$temp)## [1] 23.9 23.9 23.8 23.8 23.6 23.2barplot(station_data$temp)
Basic Data Visualization
Barplots and Pie Charts
barplot(f_temp)
Basic Data Visualization
Barplots and Pie Charts
barplot(f_temp,beside=TRUE,horiz=TRUE,las=1,
main="Frequency of Station Temperature",
names.arg=c("T"),legend.text=c("TEMP-f"),
args.legend=list(x="bottomright"))
Basic Data Visualization
Barplots and Pie Charts
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.5.2qplot(factor(station_data$temp),geom="bar")
Basic Data Visualization
Barplots and Pie Charts
head(station_data$precipitation)
pie(table(station_data$precipitation),labels=c("V1","V2","V3","V4","V5"),col=c("white","blue","green","orange"),main="pie chart for precipitation")
Basic Data Visualization
Histogram
hist(station_data$temp)
Basic Data Visualization
Histogram
hist(station_data$tem,breaks=seq(19,32,1),col="green",main="Temp",xlab="HP")
abline(v=c(mean(station_data$temp),median(station_data$temp)), col=c("blue","red"),lty=c(2,3),lwd=2)
legend("topright",legend=c("mean T","median T"),lty=c(2,3),lwd=2,col=c("blue","red"))
Basic Data Visualization
Histogram
qplot(station_data$temp,geom="blank",main="Temp Hist",xlab="Temp")+
geom_histogram(color="black",fill="white",breaks=seq(19,32,1),closed="right") +
geom_vline(mapping=aes(xintercept=c(mean(station_data$tem), median(station_data$tem)), linetype=factor(c("mean","median"))) , col=c("blue","red"),show.legend=TRUE)+
scale_linetype_manual(values=c(2,3)) +
labs(linetype="")
Basic Data Visualization
Boxplot
boxplot(station_data$temp)
Basic Data Visualization
Boxplot
Basic Data Visualization
Scatter Plots
plot(station_data$temp,station_data$relative_humidity)
Basic Data Visualization
library("GGally")
ggpairs(station_data[,7:10],axisLabels="internal")